Currently increasingly more mobile devices are equipped with digital cameras for capturing still images and video. The quality of images and video improves continuously but there is still a need for specific applications to evaluate the quality. Image quality estimation is an important task for example for mobile printing, the processing of a large image database, memory saving and the recapturing of an image. In mobile printing, a portable printer can be directly connected to the mobile device and the image can be printed directly without transferring it to a personal computer (PC). In connection with a large image database, the image quality estimation can be applied in order to automatically delete images having a low quality. On the other hand, when image capturing is in process, images having a low quality are not necessarily saved in the memory of the mobile device but the user can be asked to recapture the image.
Among many digital imaging applications incorporated in mobile devices, the printing capability can be considered one of the most important ones. Miniaturized printers suitable to be connected to the mobile devices are already available on the market. By means of such printers, images captured by a mobile device can be directly printed without transferring them to a PC, when such a printer is connected to the mobile device. One of the main problems in mobile printing is the image quality. In mobile printing, the image is not transferred to the PC but is printed directly from the mobile device; therefore, the quality of the captured image cannot be accurately verified. Even though the image is shown on the display of the mobile device prior to the printing process, the resolution of the display is smaller than the resolution of the image, which may make the image having a low quality look like an image having a high quality on the display. In addition, the image that is shown on the display of the mobile device is usually pre-processed in order to attenuate non-linearities of the display and to adapt to the display characteristics. Both of these factors may affect the image in such a manner that low quality images are seen as high quality images when shown on the display of the mobile device. In consequence, methods for image quality estimation are of great interest for mobile printing.
As stated earlier, other applications that can apply image quality estimation include large image database processing and memory management. In the case of large image database processing, an automatic mechanism to verify the image quality can be used and the images that have low quality can be automatically deleted from the database. In this manner, the database will be cleared without user interference. In the case of the memory management application, the image quality estimation can be used to define the quality of every captured image. Immediately after the image has been captured, a parameter quantifying the image quality on a certain scale (from poor quality to excellent quality) is shown on the display of the mobile device and the user is asked whether to save the image or not. In this manner, the low quality images will not be saved in the memory, wherein the memory will be saved for better images. Moreover, when a low quality image is detected, the user can choose to capture an image of the same scene again (with different parameters or a different viewing angle) so that the information contained in the scene will not be lost. Also the image quality can be used for instance to choose a quality factor in a JPEG encoder in order to obtain the best trade-off between the memory usage and the visual quality of the encoded images.
As stated above, the image quality estimation can be incorporated in a mobile device having an imaging capability. However, in addition to mobile devices, the image quality estimation can be incorporated in digital cameras or similar devices. Many image processing algorithms and applications can use information on the quality of the captured image to improve the performance of such algorithms and applications.
Conventional methods for image quality estimation can be categorized in three main classes. The first class is Full-Reference Quality Metric also known as a fidelity metric. The algorithms of this class require an original undistorted image to be available. Usually mean squared error (MSE), peak signal to noise ratio (PSNR) or other metrics are computed to quantize the difference between the distorted image and the undistorted image. This class of image estimation methods is mainly used to estimate the performances of the restoration algorithms. In such a case an original undistorted image is artificially distorted and the distorted image is processed by a certain algorithm. The output image is then compared with the original undistorted image.
The second class of metrics is called Reduced-Reference Quality Metric. The algorithms of this class compute statistics from the distorted image and from the undistorted image. The quality of the distorted image is estimated on the basis of the comparison of these statistics. The original undistorted image is not available in some applications in practice, and therefore methods for image quality applications that do not require the use of the original image are necessary.
The third class of metrics is called No-Reference Quality Metric, in which an original image is not used for image quality estimation. The image quality is computed without the need of any information about the undistorted image. Usually, several distortions are estimated from the image, such as noise level, the amount of blur, blocking artifacts etc. There are many algorithms for estimating a certain distortion in the state-of-art. For instance in the case of blur estimation, several algorithms based on signal activity measurement and on the estimation of the edge length are available.